.md
.pdf
PromptLayer
 Contents 
Installation and Setup
Wrappers
LLM
PromptLayer#
This page covers how to use PromptLayer within LangChain.
It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.
Installation and Setup#
If you want to work with PromptLayer:
Install the promptlayer python library pip install promptlayer
Create a PromptLayer account
Create an api token and set it as an environment variable (PROMPTLAYER_API_KEY)
Wrappers#
LLM#
There exists an PromptLayer OpenAI LLM wrapper, which you can access with
from langchain.llms import PromptLayerOpenAI
To tag your requests, use the argument pl_tags when instanializing the LLM
from langchain.llms import PromptLayerOpenAI
llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])
To get the PromptLayer request id, use the argument return_pl_id when instanializing the LLM
from langchain.llms import PromptLayerOpenAI
llm = PromptLayerOpenAI(return_pl_id=True)
This will add the PromptLayer request ID in the generation_info field of the Generation returned when using .generate or .agenerate
For example:
llm_results = llm.generate(["hello world"])
for res in llm_results.generations:
    print("pl request id: ", res[0].generation_info["pl_request_id"])
You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. Read more about it here.
This LLM is identical to the OpenAI LLM, except that
all your requests will be logged to your PromptLayer account
you can add pl_tags when instantializing to tag your requests on PromptLayer